Method for crushing and recycling waste diapers

Abstract

The invention discloses a method for crushing and recycling waste diapers, and relates to the technical field of resource recycling. The operation state of gravity separation equipment and the crushing condition of waste diapers are monitored in real time, and fault identification and risk analysis are performed on equipment power consumption fluctuation and material particle size distribution by utilizing feature extraction, so that early warning signals of corresponding levels are generated; the maintenance sequence is determined according to the risk level of the equipment, and the material putting amount and frequency are dynamically adjusted, so that the equipment is always operated in the optimal state; therefore, equipment overload and faults are effectively prevented, the service life of the equipment is prolonged, the maintenance cost is reduced, distribution of maintenance resources can be optimized, production stagnation caused by the equipment faults is reduced, the production efficiency and economic benefits are improved, and the continuity and high efficiency of the waste baby diaper treatment process are ensured.

Description

Technical Field

[0001] The invention relates to the technical field of resource recycling, and in particular to a method for crushing and recycling discarded diapers. Background Art

[0002] Discarded diaper crushing and recycling refers to the process of crushing discarded diapers to reduce their physical volume, and further extracting recyclable materials (such as polymer absorbent resin and cellulose) through separation technology. This method aims to reduce the pollution of discarded diapers to the environment, realize the resource utilization of waste, and turn waste into treasure.

[0003] Specifically, discarded diapers are first collected and sorted, and after impurities are removed, they are sent to pulverizing equipment for pulverization to reduce their volume. Subsequently, the pulverized materials are separated through a combination of physical and chemical methods to extract high-molecular absorbent resins and cellulose materials with recycling value. After cleaning and drying, these materials can be reused, for example, as agricultural water-retaining agents and papermaking raw materials, thus realizing the recycling of discarded diapers.

[0004] The prior art has the following deficiencies: When discarded diapers are crushed and recycled, large pieces of plastic and cellulose are separated through screens, and gravity separation equipment is used to separate the heavier polymer absorbent resin from other lighter materials. However, during the processing, if the gravity separation equipment is continuously overloaded, it may cause key components to overheat, increase wear and even collapse, causing equipment failure. At the same time, if a large-scale equipment failure occurs, it may cause the entire production line to stagnate and be unable to continue processing discarded diapers. The time required to repair the equipment may be long, which directly affects the production schedule and output, causing significant economic losses. Summary of the invention

[0005] The purpose of the present invention is to provide a method for crushing and recycling discarded diapers to overcome the shortcomings of the background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a method for crushing and recycling discarded diapers, comprising the following steps: S1: Collect the materials that pass through the screen to the feed port of the gravity separation equipment, ensure the normal operation of the gravity separation equipment, evenly transport the screened materials to the gravity separation equipment, and monitor the operation status of the equipment and the crushing of discarded diapers in real time; S2: Extract features from the monitored equipment operation status data and diaper crushing data, and identify faults of the gravity separation equipment based on the extracted power consumption fluctuation characteristics of the gravity separation equipment and the particle size distribution characteristics of the separated materials; S3: According to the result of gravity separation equipment fault identification, the gravity separation equipment is divided into two categories: potential fault equipment and normal equipment; S4: Further analyze the risk of failure events of potential faulty equipment, determine the risk level of the failure event, and generate a warning signal of the corresponding level; S5: Determine the maintenance sequence of the potential faulty equipment according to the risk of failure of the potential faulty equipment, and dynamically adjust the amount and frequency of the screened materials in the gravity separation equipment according to the maintenance sequence.

[0007] Preferably, the power consumption abnormal fluctuation index is generated according to the extracted power consumption fluctuation characteristics of the gravity separation device, and the method for obtaining the power consumption abnormal fluctuation index is: Collect the power consumption data of the equipment in time period t to form a time series x(t), remove the trend of the data with long-term trends to obtain the detrended data x'(t); Perform FFT on the preprocessed power consumption data x'(t) to obtain the frequency domain signal X(f). The specific conversion expression is: ; in, is the complex value of the frequency domain signal at frequency f, N is the total number of signal sample points, is the kernel function of the Fourier transform, where j is the imaginary unit; Calculate the power spectral density P(f), which represents the power of each frequency component, P(f)= ;in, is the amplitude of the frequency domain signal X(f), select the frequency range of interest [fmin, fmax], calculate the total power Ptotal within the frequency range and the power P(fk) at a specific frequency point, the specific calculation expression is: ; ; Calculate the abnormal fluctuation index A(fk) of each frequency point. The specific calculation expression is: ; Wherein, A(fk) is the abnormal fluctuation index at the frequency point fk, and the abnormal fluctuation index of power consumption is obtained by summing up the abnormal fluctuation index of each frequency point.

[0008] Preferably, the material particle size distribution deviation index is generated according to the extracted particle size distribution characteristics of the separated material of the gravity separation device, and the method for obtaining the material particle size distribution deviation index is: Obtain the material particle size distribution data of the gravity separation equipment in normal operation in real time to form a reference distribution P. When the equipment is running, collect the current material particle size distribution data in real time to form the current distribution Q. Calculate the reference distribution and current distribution KL divergence of the material particle size: ; Where P(i) is the probability of the reference distribution in the i-th granularity range, Q(i) is the probability of the current distribution in the i-th granularity range, The reference distribution and current distribution KL divergence values ​​of the material particle size are obtained, and the reference distribution and current distribution KL divergence values ​​of the material particle size obtained are compared with the reference distribution and current distribution KL divergence standard values ​​of the material particle size under the standard state in the historical data, and the material particle size distribution deviation index is calculated. The specific calculation expression is: ; In the formula, is the reference distribution of material particle size under standard conditions in historical data and the standard value of the current distribution KL divergence, It is the material particle size distribution deviation index.

[0009] Preferably, the power consumption abnormal fluctuation index and the material particle size distribution deviation index are normalized, and the abnormal operation coefficient of the gravity separation equipment failure is calculated by the normalized power consumption abnormal fluctuation index and the material particle size distribution deviation index.

[0010] Preferably, the acquired abnormal operation coefficient of the gravity separation device when a failure occurs is compared with a preset abnormal operation coefficient reference threshold value; if the abnormal operation coefficient of the gravity separation device when a failure occurs is greater than or equal to the preset abnormal operation coefficient reference threshold value, the probability of failure is high, and the gravity separation device is classified as a potential fault device, and an abnormal operation signal of the gravity separation device is generated; if the abnormal operation coefficient of the gravity separation device when a failure occurs is less than the preset abnormal operation coefficient reference threshold value, the probability of failure is low, and the gravity separation device is classified as a normal device, and a normal operation signal of the gravity separation device is generated.

[0011] Preferably, when the gravity separation equipment is classified as a potential fault equipment, that is, the abnormal operation coefficient generated within a period of time is greater than or equal to a preset abnormal operation coefficient reference threshold, the abnormal operation coefficients generated within a subsequent period of time that are greater than or equal to the preset abnormal operation coefficient reference threshold are collected, and a corresponding data set is established, and the abnormal operation coefficients in the data set are compared and analyzed with the abnormal operation coefficient reference threshold to calculate the risk coefficient of a failure event occurring in the potential fault equipment.

[0012] Preferably, the acquired risk coefficient of a failure event of a potential faulty device is compared with a gradient risk threshold, the gradient risk threshold includes a first risk threshold and a second risk threshold, and the first risk threshold is less than the second risk threshold, and the risk coefficient of a failure event of a potential faulty device is compared with the first risk threshold and the second risk threshold respectively; If the risk coefficient of a failure event occurring in a potential faulty device is greater than the second risk threshold, it is classified as a high risk level and a high risk warning signal is immediately generated; if the risk coefficient of a failure event occurring in a potential faulty device is greater than or equal to the first risk threshold and less than or equal to the second risk threshold, it is classified as a medium risk level and a medium risk warning signal is generated; if the risk coefficient of a failure event occurring in a potential faulty device is less than the first risk threshold, it is classified as a low risk level and a low risk warning signal is generated.

[0013] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention starts with real-time monitoring of the equipment operation status and the crushing of discarded diapers, extracts features from the acquired data, identifies potential faults, and generates warning signals according to the risk level to ensure that the gravity separation equipment operates in the best state. By dynamically adjusting the amount and frequency of screening materials, it effectively prevents equipment overload, reduces equipment failure rate, and extends equipment service life. At the same time, it rationally allocates maintenance resources, reduces production stagnation, and improves production efficiency and economic benefits.

[0014] 2. The present invention can timely detect equipment abnormalities through real-time monitoring and data analysis, prevent failures in advance, and reduce repair time and maintenance costs. The abnormal operation coefficient of the equipment is calculated through the normalized power consumption abnormal fluctuation index and material particle size distribution deviation index, and a gradient risk assessment is performed based on the risk coefficient to generate corresponding early warning signals and maintenance plans, thereby realizing intelligent management and maintenance of the equipment, ensuring the continuity and stability of the production line, and improving overall resource utilization and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0016] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] For examples, see Figure 1 As shown, the method for crushing and recycling discarded diapers described in this embodiment includes the following steps: S1: Collect the materials that pass through the screen to the feed port of the gravity separation equipment, ensure the normal operation of the gravity separation equipment, evenly transport the screened materials to the gravity separation equipment, and monitor the operation status of the equipment and the crushing of discarded diapers in real time; S2: Extract features from the monitored equipment operation status data and diaper crushing data, and identify faults of the gravity separation equipment based on the extracted power consumption fluctuation characteristics of the gravity separation equipment and the particle size distribution characteristics of the separated materials; S3: According to the result of gravity separation equipment fault identification, the gravity separation equipment is divided into two categories: potential fault equipment and normal equipment; S4: Further analyze the risk of failure events of potential faulty equipment, determine the risk level of the failure event, and generate a warning signal of the corresponding level; S5: Determine the maintenance sequence of the potential faulty equipment according to the risk of failure of the potential faulty equipment, and dynamically adjust the amount and frequency of the screened materials in the gravity separation equipment according to the maintenance sequence.

[0019] Among them, in S1, the materials passing through the screen are collected to the feed port of the gravity separation equipment to ensure the normal operation of the gravity separation equipment, and the screened materials are evenly transported to the gravity separation equipment. The operation status of the equipment and the crushing of discarded diapers are monitored in real time, mainly including: After the screen separation step, the small particles that pass through the screen (such as high molecular absorbent resins and smaller cellulose particles) are collected. These materials can be directed to an intermediate collection container or directly to the feed port of the gravity separation equipment using a conveyor belt or gravity chute. During the material collection process, any remaining large impurities are further removed to ensure smooth subsequent separation processes.

[0020] Before starting material transfer, check all parts of the gravity separation equipment to ensure there are no mechanical failures, blockages or other problems. Check the equipment's lubrication status, power connections and transmission components. Start the gravity separation equipment and observe its operating status to ensure that the equipment can operate smoothly without abnormal noise, vibration or overheating. If necessary, preheat and calibrate the equipment to achieve optimal operating conditions and ensure separation results.

[0021] Use conveyor belts, screw conveyors or other suitable conveying systems to evenly convey the collected material to the feed port of the gravity separation equipment. Ensure that the material is evenly distributed during the conveying process without accumulation or blockage. Distributors or even distribution devices can be used to control the flow rate and distribution of the material to avoid local overload. Continuous feeding: Maintain a continuous feed to ensure that the gravity separation equipment can continue to process the material without interruption or overfeeding.

[0022] Install sensors at key locations of gravity separation equipment and conveying systems to monitor the operating status of the equipment and the crushing of materials. Sensors can include temperature sensors, vibration sensors, power meters, flow meters, etc. Transmit the data collected by the sensors to the central control system in real time for analysis and monitoring. Ensure that the data acquisition system can operate stably without data loss. Install a monitoring interface in the control room or operating table to display the operating status of the equipment and material processing in real time. Operators can view the equipment's temperature, vibration, power consumption and other parameters through the monitoring interface to detect abnormalities in a timely manner.

[0023] It should be noted here that in the process of crushing and recycling discarded diapers, the materials passing through the screen are collected at the feed port of the gravity separation equipment to ensure the continuity and efficiency of the separation process. By ensuring the normal operation of the gravity separation equipment, downtime and reduced processing efficiency caused by equipment failure can be avoided. Uniform material transportation helps to maintain the stability of the separation effect and prevent local overload or insufficient feed from affecting the separation quality. Real-time monitoring of the operating status and crushing of the equipment can promptly identify and solve potential problems, ensure the smooth progress of the entire recycling process, and improve the efficiency and quality of discarded diaper processing.

[0024] S2: Feature extraction is performed on the monitored equipment operation status data and diaper crushing data respectively, and fault identification of the gravity separation equipment is performed based on the extracted power consumption fluctuation characteristics of the gravity separation equipment and the particle size distribution characteristics of the separated materials.

[0025] Various sensors installed on gravity separation equipment and crushing equipment can monitor the equipment's operating status (such as power, temperature, vibration, etc.) and material crushing conditions (such as particle size distribution) in real time. The data acquisition system can continuously record equipment operating status data and crushing data to ensure data accuracy and completeness.

[0026] The power consumption fluctuation feature extraction includes: Process the equipment power consumption data, remove noise and outliers, obtain clean time series data, and analyze the fluctuation of power consumption. Perform spectrum analysis on the power consumption data, extract the frequency components of the power consumption fluctuation characteristics of the gravity separation equipment, and identify periodic fluctuations and abnormal frequency peaks.

[0027] Particle size distribution feature extraction includes: using a particle size analyzer to measure the particle size of the separated material, recording the number and distribution of particles in different particle size ranges, extracting the particle size distribution characteristics of the separated material to analyze the changes in the particle size distribution. If the equipment allows, particle morphology characteristics such as aspect ratio, circularity, etc. can also be extracted to further evaluate the crushing effect.

[0028] The power consumption abnormal fluctuation index is generated according to the extracted power consumption fluctuation characteristics of the gravity separation equipment. The method for obtaining the power consumption abnormal fluctuation index is: Collect the power consumption data of the equipment in the time period t to form a time series x(t). If there is a long-term trend in the data, use a detrending method (such as the difference method) to remove the trend and obtain the detrended data x'(t); Perform FFT on the preprocessed power consumption data x'(t) to obtain the frequency domain signal X(f). The specific conversion expression is: ; in, is the complex value of the frequency domain signal at frequency f, N is the total number of signal sample points, is the kernel function of the Fourier transform, where j is the imaginary unit; Calculate the power spectral density P(f), which represents the power of each frequency component, P(f)= ;in, is the amplitude (modulus) of the frequency domain signal X(f), select the frequency range of interest [fmin, fmax], calculate the total power Ptotal within the frequency range and the power P(fk) at a specific frequency point, the specific calculation expression is: ; ; Calculate the abnormal fluctuation index A(fk) of each frequency point, which represents the ratio of the power of the frequency point to the total power. The specific calculation expression is: ; Wherein, A(fk) is the abnormal fluctuation index at the frequency point fk, and the abnormal fluctuation index of power consumption is obtained by summing up the abnormal fluctuation index of each frequency point.

[0029] The larger the power consumption abnormal fluctuation index is, the higher the possibility of failure of the gravity separation equipment is. This is because abnormal fluctuations in power consumption usually reflect the instability of the internal operation of the equipment and abnormal load conditions, such as increased friction, wear or blockage of mechanical parts, which can cause drastic changes in the power consumption of the equipment.

[0030] During normal operation of gravity separation equipment, power consumption should remain within a relatively stable range with only a small amount of fluctuation. If the abnormal power consumption fluctuation index is monitored to increase significantly, it indicates that there may be problems in some parts of the equipment. These abnormal fluctuations may be caused by mechanical wear, blockage, loose components or electrical failures, causing the equipment to consume more power to maintain operation, thus showing the characteristics of abnormal fluctuations.

[0031] Therefore, by monitoring and analyzing the abnormal fluctuation index of power consumption, potential equipment failures can be identified in time to prevent equipment shutdown or damage due to failures. The earlier these abnormal fluctuations are discovered and handled, the more stable operation of the equipment can be ensured and the service life of the equipment can be extended, thus avoiding production stagnation and economic losses caused by failures.

[0032] The material particle size distribution deviation index is generated according to the extracted particle size distribution characteristics of the separated material of the gravity separation equipment. The method for obtaining the material particle size distribution deviation index is: Obtain the material particle size distribution data of the gravity separation equipment in normal operation in real time to form a reference distribution P. When the equipment is running, collect the current material particle size distribution data in real time to form the current distribution Q. Calculate the reference distribution and current distribution KL divergence of the material particle size: ; Where P(i) is the probability of the reference distribution in the i-th granularity range, Q(i) is the probability of the current distribution in the i-th granularity range, The reference distribution and current distribution KL divergence values ​​of the material particle size are obtained, and the reference distribution and current distribution KL divergence values ​​of the material particle size obtained are compared with the reference distribution and current distribution KL divergence standard values ​​of the material particle size under the standard state in the historical data, and the material particle size distribution deviation index is calculated. The specific calculation expression is: ; In the formula, is the reference distribution of material particle size under standard conditions in historical data and the standard value of the current distribution KL divergence, It is the material particle size distribution deviation index.

[0033] The larger the deviation index of the material particle size distribution, the higher the possibility of failure of the gravity separation equipment. This is because under normal operating conditions, the gravity separation equipment should be able to stably and effectively separate materials by particle size and maintain a relatively consistent particle size distribution. If the deviation index increases significantly, it indicates that there is a large difference between the current particle size distribution and the normal reference distribution. This may be due to an abnormality or failure inside the equipment, resulting in a decrease in separation effect.

[0034] Specifically, the reasons for the increase in the particle size distribution deviation index may include wear and blockage of the mechanical parts of the equipment, changes in fluid dynamic conditions or improper operation. These problems will directly affect the separation effect of the material and cause abnormal particle size distribution of the separated material. Therefore, when the particle size distribution deviation index is monitored to be large, it means that the separation performance of the equipment has been affected, and the equipment needs to be inspected and maintained in time to prevent the fault from further deteriorating.

[0035] By real-time monitoring and analyzing the particle size distribution deviation index of materials, potential equipment failures can be discovered early, and preventive maintenance measures can be taken to avoid production stagnation and quality problems caused by equipment failures. This not only helps to improve the stability and reliability of equipment operation, but also extends the service life of the equipment and ensures the continuity and efficiency of the production process.

[0036] The power consumption abnormal fluctuation index and the material particle size distribution deviation index are normalized, and the abnormal operation coefficient of the gravity separation equipment failure is calculated by the normalized power consumption abnormal fluctuation index and the material particle size distribution deviation index.

[0037] For example, the present invention can use the following formula to calculate the abnormal operation coefficient of the gravity separation equipment when a fault occurs, and the calculation expression is: ; In the formula, is the abnormal operation coefficient, is the power consumption abnormal fluctuation index, is the material particle size distribution deviation index, are the proportional coefficients of the abnormal fluctuation index of power consumption and the deviation index of material particle size distribution, respectively, and 0.

[0038] S3: According to the result of gravity separation equipment fault identification, the gravity separation equipment is divided into two categories: potential fault equipment and normal equipment.

[0039] The acquired abnormal operation coefficient of the gravity separation device failure is compared with the preset abnormal operation coefficient reference threshold. If the abnormal operation coefficient of the gravity separation device failure is greater than or equal to the preset abnormal operation coefficient reference threshold, the higher the possibility of abnormal operation of the gravity separation device, the greater the probability of failure. The gravity separation device is classified as a potential fault device, and an abnormal operation signal of the gravity separation device is generated. If the abnormal operation coefficient of the gravity separation device failure is less than the preset abnormal operation coefficient reference threshold, the lower the possibility of abnormal operation of the gravity separation device, the lower the probability of failure. The gravity separation device is classified as a normal device, and a normal operation signal of the gravity separation device is generated.

[0040] S4: Further analyze the risk of failure events of potential faulty equipment, determine the risk level of the failure event, and generate a warning signal of the corresponding level; When the gravity separation equipment is classified as a potential faulty equipment, that is, the abnormal operation coefficient generated within a period of time is greater than or equal to the preset abnormal operation coefficient reference threshold, the abnormal operation coefficients generated within a subsequent period of time that are greater than or equal to the preset abnormal operation coefficient reference threshold are collected, and a corresponding data set is established. The abnormal operation coefficients in the data set are compared and analyzed with the abnormal operation coefficient reference threshold to calculate the risk coefficient of a failure event occurring in the potential faulty equipment.

[0041] The preset abnormal operation coefficient reference threshold is marked as , the abnormal operation coefficients generated in the subsequent period of time that are greater than or equal to the preset abnormal operation coefficient reference threshold value establish a corresponding data set ={ , , ,..., ,..., },in, , is a positive integer greater than 0, where a is the number of the abnormal operation coefficient in the data set; the abnormal operation coefficient in the data set is compared and analyzed with the abnormal operation coefficient reference threshold, and the risk coefficient of the potential fault equipment failure event is calculated. The specific calculation expression is: ; In the formula, is the risk factor.

[0042] Compare the acquired risk coefficient of a failure event of a potential faulty device with a gradient risk threshold, the gradient risk threshold includes a first risk threshold and a second risk threshold, and the first risk threshold is less than the second risk threshold, and compare the risk coefficient of a failure event of a potential faulty device with the first risk threshold and the second risk threshold respectively; If the risk factor of a potential faulty device failure event is greater than the second risk threshold, it means that the risk of the device failure is extremely high, and it is classified as a high-risk level, and a high-risk warning signal is immediately generated; If the risk factor of a potential faulty device having a fault event is greater than or equal to the first risk threshold and less than or equal to the second risk threshold, it indicates that the device has a certain risk, and is classified as a medium risk level, and a medium risk warning signal is generated; If the risk factor of a failure event occurring in a potential faulty device is less than the first risk threshold, it means that the device is operating basically normally and the risk is low. It is classified as a low-risk level and a low-risk warning signal is generated.

[0043] S5: Determine the maintenance sequence of the potential faulty equipment according to the risk of failure of the potential faulty equipment, and dynamically adjust the amount and frequency of the screened materials in the gravity separation equipment according to the maintenance sequence.

[0044] Based on the results of the risk assessment, determine the risk level of the potential faulty equipment and classify them into high, medium, and low risks. Determine the priority of maintenance based on the risk level of the equipment. Generally, high-risk equipment is handled first, followed by medium-risk equipment, and finally low-risk equipment.

[0045] The specific steps to determine the maintenance sequence are: High-risk equipment: arrange maintenance immediately and give priority to it.

[0046] Medium-risk equipment: Arrange for inspection and maintenance after high-risk equipment is overhauled.

[0047] Low-risk equipment: Included in the regular maintenance plan for regular inspection and maintenance.

[0048] Dynamically adjust the amount and frequency of screening materials. During equipment maintenance, in order to maintain the efficient operation of the gravity separation equipment, it is necessary to dynamically adjust the amount and frequency of screening materials according to the maintenance status of the equipment.

[0049] During maintenance of high-risk equipment: reduce the amount and frequency of material added to prevent equipment overload or greater failure.

[0050] During the maintenance of medium-risk equipment: appropriately adjust the amount and frequency of material delivery to keep the equipment running within the safe load range.

[0051] Normal operation of low-risk equipment: maintain or slightly increase the amount and frequency of material delivery to improve the processing efficiency of the equipment.

[0052] When high-risk equipment is under maintenance, the amount of material delivered can be reduced to reduce the load on the equipment and prevent new failures. By reducing the frequency of material delivery and extending the interval between each delivery, the equipment can have more time to handle the current load.

[0053] Monitor the equipment's operating status in real time, and adjust the delivery volume and frequency at any time according to actual conditions to ensure that the equipment is in the best operating state.

[0054] In this embodiment, the material passing through the screen is collected to the feed port of the gravity separation device to ensure the normal operation of the gravity separation device, and the screened material is evenly transported to the gravity separation device, and the operation status of the device and the crushing of the discarded diapers are monitored in real time; the monitored equipment operation status data and diaper crushing data are respectively feature extracted, and the gravity separation device is fault identified according to the extracted power consumption fluctuation characteristics of the gravity separation device and the particle size distribution characteristics of the separated materials; according to the results of the gravity separation device fault identification, the gravity separation device is divided into two categories: potential fault equipment and normal equipment; the risk of the potential fault equipment failure event is further analyzed to determine the risk level of the failure event, and generate a corresponding level of early warning signal; according to the risk of the potential fault equipment failure, the maintenance order of the potential fault equipment is determined, and the amount and frequency of the screened material in the gravity separation device are dynamically adjusted according to the maintenance order. It can realize all-round monitoring and management of the gravity separation equipment, improve the stability and efficiency of the equipment operation, reduce the failure rate and maintenance cost, and ensure the continuity and stability of the production process. It helps to improve overall production efficiency, reduce resource waste, and enhance the company's economic benefits and market competitiveness.

[0055] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0056] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0057] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0058] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0059] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0060] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0061] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0062] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for crushing and recycling discarded diapers, characterized in that: The following steps are involved: S1: Collect the materials that pass through the screen to the feed port of the gravity separation equipment, ensure the normal operation of the gravity separation equipment, evenly transport the screened materials to the gravity separation equipment, and monitor the operation status of the equipment and the crushing of discarded diapers in real time; S2: Extract features from the monitored equipment operation status data and diaper crushing data, and identify faults of the gravity separation equipment based on the extracted power consumption fluctuation characteristics of the gravity separation equipment and the particle size distribution characteristics of the separated materials; S3: According to the result of gravity separation equipment fault identification, the gravity separation equipment is divided into two categories: potential fault equipment and normal equipment; S4: Further analyze the risk of failure events of potential faulty equipment, determine the risk level of the failure event, and generate a warning signal of the corresponding level; S5: Determine the maintenance sequence of the potential faulty equipment according to the risk of failure of the potential faulty equipment, and dynamically adjust the amount and frequency of the screened materials in the gravity separation equipment according to the maintenance sequence.

2. The method for crushing and recycling discarded diapers according to claim 1, characterized in that: The power consumption abnormal fluctuation index is generated according to the extracted power consumption fluctuation characteristics of the gravity separation equipment. The method for obtaining the power consumption abnormal fluctuation index is: Collect the power consumption data of the equipment in time period t to form a time series x(t), remove the trend of the data with long-term trends to obtain the detrended data x'(t); Perform FFT on the preprocessed power consumption data x'(t) to obtain the frequency domain signal X(f). The specific conversion expression is: ； in, is the complex value of the frequency domain signal at frequency f, N is the total number of signal sample points, is the kernel function of the Fourier transform, where j is the imaginary unit; Calculate the power spectral density P(f), which represents the power of each frequency component, P(f)= ;in, is the amplitude of the frequency domain signal X(f), select the frequency range of interest [fmin, fmax], calculate the total power Ptotal within the frequency range and the power P(fk) at a specific frequency point, the specific calculation expression is: ； ； Calculate the abnormal fluctuation index A(fk) of each frequency point. The specific calculation expression is: ； Wherein, A(fk) is the abnormal fluctuation index at the frequency point fk, and the abnormal fluctuation index of power consumption is obtained by summing up the abnormal fluctuation index of each frequency point.

3. The method for crushing and recycling discarded diapers according to claim 2, characterized in that: The material particle size distribution deviation index is generated according to the extracted particle size distribution characteristics of the separated material of the gravity separation equipment. The method for obtaining the material particle size distribution deviation index is: Obtain the material particle size distribution data of the gravity separation equipment in normal operation in real time to form a reference distribution P. When the equipment is running, collect the current material particle size distribution data in real time to form the current distribution Q. Calculate the reference distribution and current distribution KL divergence of the material particle size: ； Where P(i) is the probability of the reference distribution in the i-th granularity range, Q(i) is the probability of the current distribution in the i-th granularity range, The reference distribution and current distribution KL divergence values ​​of the material particle size are obtained, and the reference distribution and current distribution KL divergence values ​​of the material particle size obtained are compared with the reference distribution and current distribution KL divergence standard values ​​of the material particle size under the standard state in the historical data, and the material particle size distribution deviation index is calculated. The specific calculation expression is: ； In the formula, is the reference distribution of material particle size under standard conditions in historical data and the standard value of the current distribution KL divergence, It is the material particle size distribution deviation index.

4. The method for crushing and recycling discarded diapers according to claim 3, characterized in that: The power consumption abnormal fluctuation index and the material particle size distribution deviation index are normalized, and the abnormal operation coefficient of the gravity separation equipment failure is calculated by the normalized power consumption abnormal fluctuation index and the material particle size distribution deviation index.

5. The method for crushing and recycling discarded diapers according to claim 4, characterized in that: The acquired abnormal operation coefficient of the gravity separation device failure is compared with a preset abnormal operation coefficient reference threshold. If the abnormal operation coefficient of the gravity separation device failure is greater than or equal to the preset abnormal operation coefficient reference threshold, the probability of failure is high, and the gravity separation device is classified as a potential failure device. At this time, an abnormal operation signal of the gravity separation device is generated; If the abnormal operation coefficient of the gravity separation device failure is less than the preset abnormal operation coefficient reference threshold, the probability of failure is small, and the gravity separation device is classified as a normal device. At this time, a gravity separation device normal operation signal is generated.

6. The method for crushing and recycling discarded diapers according to claim 5, characterized in that: When the gravity separation equipment is classified as a potential faulty equipment, that is, the abnormal operation coefficient generated within a period of time is greater than or equal to the preset abnormal operation coefficient reference threshold, the abnormal operation coefficients generated within a subsequent period of time that are greater than or equal to the preset abnormal operation coefficient reference threshold are collected, and a corresponding data set is established. The abnormal operation coefficients in the data set are compared and analyzed with the abnormal operation coefficient reference threshold to calculate the risk coefficient of a failure event occurring in the potential faulty equipment.

7. The method for crushing and recycling discarded diapers according to claim 6, characterized in that: Compare the acquired risk coefficient of a failure event of a potential faulty device with a gradient risk threshold, the gradient risk threshold includes a first risk threshold and a second risk threshold, and the first risk threshold is less than the second risk threshold, and compare the risk coefficient of a failure event of a potential faulty device with the first risk threshold and the second risk threshold respectively; If the risk factor of a potential fault device failure event is greater than the second risk threshold, it is classified as a high risk level and a high risk warning signal is immediately generated; If the risk factor of a potential fault device failure event is greater than or equal to the first risk threshold and less than or equal to the second risk threshold, it is classified as a medium risk level and a medium risk warning signal is generated; If the risk factor of a failure event of a potential faulty device is less than a first risk threshold, it is classified as a low risk level and a low risk warning signal is generated.

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